Wavelength multiplexed optical networks are continually increasing in both functionality and complexity. This increased functionality is driven by, among other things, the provision of optical add/drop multiplexers and optical switching elements that permit a rich variety wavelength connectivity and exchange between fibers and nodes in optical networks. The increased complexity is driven by, among other things, increasing bit rates of individual channels, increasing channel counts, increasing channel densities, and increasing transmission distances.
The demands placed by such optical networks on signal quality mandate the use of specialized tools to calculate signal impairments as they propagate between origination and termination points. These signal impairments are associated with a wide range of linear and nonlinear effects which can act on a single wavelength channel or couple multiple wavelength channels.
Conventional approaches to optical signal propagation modeling can be grouped into two broad categories. First, there are fully numeric approaches. These approaches solve electromagnetic wave propagation equations, and generally account for nonlinear effects in the propagation medium (i.e. fiber). The resources required for such approaches, including computer memory requirements and computational time, grow rapidly with increased system complexity. Given the current state of desktop computers, it takes several hours to simulate the propagation of a relatively few 10 Gb/s channels over ˜1000 km of fiber. Desktop computer power is not sufficient for simulations involving more than ˜16 channels of 10 Gb/s each. Typical commercial systems can have up to 192 channels of 10 Gb/s each, and a typical North American fiber network extends over ˜25000 km. Further, network performance optimization can require propagation to be recomputed several times as an optimal solution is sought. For example, signal power can be changed, as well as the placement of signal conditioning elements, such as dispersion compensation modules, optical amplifiers, and the like, Signal channel spacing, bit rate, etc. can also be changed. Clearly, an alternative approach to optical signal propagation modeling is needed.
Second, there are semi-analytic or empirical approaches to optical signal propagation modeling. These approaches typically divide signal propagation into separate components, each component associated with a particular propagation effect. Examples of such propagation effects are Amplified Spontaneous Emission (ASE) noise accumulation associated with optical amplifiers, Self-Phase Modulation (SPM) associated with single-channel fiber nonlinearity, Cross-Phase Modulation (XPM) associated with a fiber nonlinearity coupling multiple adjacent channels, and the like. Each effect can be assumed to be independent of the others if each contributes only a small overall distortion to the signal. Calculations are typically carried out on a complete end-to-end link, starting at the point where an optical signal is generated and ending at the electrical receiver. In general, the semi-analytic or empirical approaches to optical signal propagation modeling provide computational efficiency, but sacrifice accuracy. One area of deficiency associated with these approaches involves their application to richly interconnected optical networks. A small change in one area of a network can impact optically coupled signals spanning a large geographic area, and thus require extensive recomputation.
In general, conventional approaches to optical signal propagation modeling have the following limitations which preclude their use in richly interconnected optical networks: 1) they assume that all wavelength signals have the same origination and termination points; 2) they account for nonlinear effects simultaneously (i.e. no differentiation); 3) they are computationally impractical for systems with fully populated channels; 4) they make optimization very difficult, if not impossible, as small configuration changes require full recomputation; and 5) they do not lend themselves to distributed calculations (i.e. parallelized calculations).
Thus, what is needed is a novel approach that overcomes the above limitations, while still providing sufficient accuracy.